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A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments

Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a part...

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Autores principales: Curreli, Cristina, Di Salvatore, Valentina, Russo, Giulia, Pappalardo, Francesco, Viceconti, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9483464/
https://www.ncbi.nlm.nih.gov/pubmed/36115895
http://dx.doi.org/10.1007/s10439-022-03078-w
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author Curreli, Cristina
Di Salvatore, Valentina
Russo, Giulia
Pappalardo, Francesco
Viceconti, Marco
author_facet Curreli, Cristina
Di Salvatore, Valentina
Russo, Giulia
Pappalardo, Francesco
Viceconti, Marco
author_sort Curreli, Cristina
collection PubMed
description Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose–response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40‐2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development.
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spelling pubmed-94834642022-09-19 A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments Curreli, Cristina Di Salvatore, Valentina Russo, Giulia Pappalardo, Francesco Viceconti, Marco Ann Biomed Eng S.I. : Modeling for Advancing Regulatory Science Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose–response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40‐2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development. Springer International Publishing 2022-09-17 2023 /pmc/articles/PMC9483464/ /pubmed/36115895 http://dx.doi.org/10.1007/s10439-022-03078-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle S.I. : Modeling for Advancing Regulatory Science
Curreli, Cristina
Di Salvatore, Valentina
Russo, Giulia
Pappalardo, Francesco
Viceconti, Marco
A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments
title A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments
title_full A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments
title_fullStr A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments
title_full_unstemmed A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments
title_short A Credibility Assessment Plan for an In Silico Model that Predicts the Dose–Response Relationship of New Tuberculosis Treatments
title_sort credibility assessment plan for an in silico model that predicts the dose–response relationship of new tuberculosis treatments
topic S.I. : Modeling for Advancing Regulatory Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9483464/
https://www.ncbi.nlm.nih.gov/pubmed/36115895
http://dx.doi.org/10.1007/s10439-022-03078-w
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